Some electric utility customers, most commonly commercial and industrial customers, are billed two separate charges on their electricity service bill—a consumption charge and a peak demand charge. The consumption charge reflects the total amount of energy that the customer uses over the billing period. For example, if the customer's site consumes 1000 kilowatt-hours (kWh) at a cost of $0.07 per kWh, the customer's consumption charge will be 1000×$0.07, or $70. In contrast, the peak demand charge reflects the highest, or peak, amount of power demanded by the customer within the billing period. For example, if the customer's site reaches a peak power demand of 20 kilowatts (kW) at a cost of $8 per kW, the customer's peak demand charge will be 20×$8, or $160. In practice, utility companies usually average power demand over recurring “demand intervals” (e.g., every 15 minutes), and then use the highest demand interval average within the billing period to calculate the peak demand charge.
The rationale for a dual consumption/demand billing scheme is that the amount of power required by each customer over time can vary widely. For instance, some customers may need large bursts of electricity on an occasional basis, while others need lesser amounts constantly. As a result, utility companies must operate and maintain sufficient generation, transmission and distribution equipment (e.g., transformers, wires, substations, etc.) at all times in order to meet potential aggregate power demand during high demand periods, even if such equipment is under-utilized the rest of the time. These operational/maintenance costs are passed on to customers proportionally, based on their peak power requirements, in the form of the peak demand charge.
For customers that face a high peak demand charge each billing cycle, it can be economical to install an onsite energy storage system (e.g., a battery-based system) that performs “peak shaving.” This means that the energy storage system discharges energy during intervals of high site load, thereby offsetting energy consumption from the utility grid and reducing, or shaving, the site's peak power demand. However, existing algorithms for controlling the flow of energy to/from such systems to achieve peak shaving (known as “peak shaving algorithms”) generally suffer from a number of drawbacks. For instance, some peak shaving algorithms are implemented using complex predictive techniques (e.g., machine learning, neural networks, etc.) that are computationally expensive and thus are difficult to deploy/execute without expensive equipment. Other peak shaving algorithms can provide effective results in certain well-tested scenarios, but are “unstable” and thus may do a relatively poor job in reducing peak power demand in other, more general use cases. Accordingly, it would be desirable to have an improved peak shaving algorithm that addresses the deficiencies (e.g., high computational cost, poor robustness, etc.) of prior art solutions.